Artificial Intelligence/Machine Learning Archives | Page 3 of 12

Innovation without Breaking the Bank. Yes, it’s Possible.

September 18, 2018/in Artificial Intelligence/Machine Learning, IT Process Automation /by Gabby Nizri

In a recent survey conducted by IDC, 45% of CIOs and senior IT executives stated that one of their top three objectives is to “create competitive advantage for the business.” Today’s IT leaders are, perhaps more than ever before, being asked to deliver more. More reliability. Faster responsiveness. Greater flexibility. They are expected to leverage digital transformation to create disruption. And they are expected to do all of this while keeping financial impact at a minimum.

These IT leaders find themselves facing quite the challenge: finding a way to continuously innovate in the face of increasing budgetary restraints. The good news is, with the right strategy, this is – indeed – possible. Here’s how.

Leverage Your Data

Today’s forward-thinking organizations are increasingly driving business value and seeking to achieve competitive advantage through deeper data insights. This enables business leaders to uncover hidden value in existing services or products. With the right approach, innovation doesn’t have to be expensive. Simply prioritize the results of data discovery in order of those initiatives that require minimal risk and investment but have the potential for high returns.

Ultimately, the key to innovation lies in having a data-oriented mindset. Initiatives that focus on reducing costs and streamlining processes can be successful, but only when the right data is being used the right way to facilitate better decision-making. The ability to rapidly execute on those findings is also important.

Review Risks and Seek Out Greater Efficiency

Another critical key to cost-effective innovation is risk analysis and efficiency planning. Review the risks the business has and assess the impact and likelihood those risks may have on customers and operations. From there, you can prioritize and monetize, making more informed decisions about which areas budget cuts can be made while still meeting business needs.

Budget cuts enable business leaders to focus their efforts on maximizing efficiency. Establishing leaner teams, optimizing process management and improving the effectiveness of certain key elements, such as data storage, can all result in a better product or service at a lower cost. And, of course, adoption of technologies, such as intelligent automation (which we touch on in greater detail below), to help streamline processes and maximize efficiency.

Optimize, Optimize and Optimize Some More

The most innovative organizations – especially those on tight budgets – make a concerted effort to continuously optimize ongoing operations and costs. It’s never a “set it and forget it” kind of thing. It’s a never-ending battle – one for which technology can provide a leg up.

IT leaders must take advantage of targeted applications and cloud opportunities to extend and enhance their core capabilities. By leveraging the power of tools like artificial intelligence and machine learning, operations can be fully optimized. Not only does this create a more productive, efficient work environment, but it also frees up human workers to apply their creative, cognitive skills toward innovative business initiatives.

According to Gartner, 90% of the IT budget is currently consumed by the support of legacy systems. These costs can be dramatically lowered through IT optimization. Adopting the right technologies – preferably ones that seamlessly integrate with legacy systems – can reduce expenditure, improve customer service and provide distinct competitive advantage. At the end of the day, innovation takes funding and resources. IT optimization can free up both.

Looking for a platform that can bring your legacy systems together, improve efficiency and help you harness the power of data for better decision-making support? Ayehu offers all of this and more. Experience it for yourself. Request a live demo today and get your organization on the path to affordable innovation.

ITSM: The 5 Keys to Intelligent Automation Adoption

One of the biggest hurdles those in IT service management face is the misconception that all the necessary tools and information for success are already available, understood and being put into practice. As such, introducing intelligent automation is often met with resistance that hinders progress and impedes the ability to further improve operations. So, what’s the solution? Well, it starts with good communication. To follow are five steps to help establish a foundation of consistent, open and organization-changing communication that will facilitate positive results.

Don’t take on too much.

You don’t have to solve all of your organizational problems in one sitting, so don’t wear yourself or your team out attempting to do so. Define your goals and set manageable milestones, incorporating intelligent automation into the mix. Then communicate those smaller, incremental objectives to the team via open-plan meetings that keep everyone in the loop. Over time, you’ll begin to figure out what works and what doesn’t so you can improve the meeting process moving forward.

Leave management out of the mix.

Obviously having IT leaders heading up key projects is important, but often times – particularly in a group meeting-type setting – having management present can be intimidating, ultimately hindering progress. The goal is to facilitate more open, honest communication and a more positive relationship with intelligent automation, so it may be wise to hold at least some of the scheduled gatherings sans management representatives. When front-line workers are free to express themselves, it can help to identify new and better ways to improve operations.

Keep things short, simple and focused.

The goal of meetings is to improve communication, but if these gatherings drag on and on without clear focus, they’ll have the opposite effect. Remember, the reason for implementing intelligent automation is to make the lives of IT personnel easier and make operations more efficient. Design meetings with the same purpose in mind. Have an agenda and encourage attendees to arrive with their ideas already prepared. A round-robin type setting where everyone has a set amount of time to share their thoughts and sell their ideas can keep things moving smoothly and on schedule to maximize everyone’s time.

Keep the conversation going.

Ideas, thoughts, feedback and suggestions don’t only arise just prior to or during a meeting, so make sure you’re making it easy and straightforward to keep the conversation going by creating an avenue where people can share and engage with one another any time the need arises. It can also be helpful to have a source of documented ideas to refer back to. Some companies use a Wiki or other open-source forum tool. Others use an enterprise social network that is devoted to all things intelligent automation related. Whatever happens to work for your group, get on it.

Take action.

Ideas and suggestions are great, but they won’t do you or anyone else any good until you actually put them into action. By facilitating open communication and inviting your team members to share their thoughts and feedback, you’ll have a pool of valuable data from which to start building out some intelligent automation initiatives. The best part is, when employees see that their voices are heard and that their opinions make a difference, it will further promote and foster communication going forward.

Support your great communication policy with a powerful intelligent automation tool. Request a product demo of Ayehu today.

7 Tips for Reducing IT Personnel Turnover

September 12, 2018/in Artificial Intelligence/Machine Learning, IT Process Automation /by Gabby Nizri

As any IT leader will acknowledge, attracting top talent is only half the battle. It’s keeping them on that’s the real challenge. And with an average employee tenure of only about 3 years, it’s a serious concern for many organizations across the globe. Add in the complex, fast paced and highly stressful role of IT and you’ve got quite the conundrum. So, what’s the secret? How can you keep your talented employees on for the long haul? Here are 7 tips to point you in the right direction.

Keep them challenged. The last thing you want is for your IT personnel to become bored and stagnant in their current positions. Avoid this by investing in ongoing training, setting up mentoring programs, and offering opportunities to learn about and master new strategies and technologies. The more you keep your IT employees engaged and involved, the less likely they’ll be to look elsewhere.

Rotate project time. Being stuck on the same project day in and day out can lead to fatigue and frustration. Consider rotating employees onto various IT projects so that they don’t feel stuck. This will provide exposure to and the opportunity to learn about new skills and also open up the door to be able to approach long-term projects from differing perspectives – both of which can benefit your organization.

Give them the tools they need. These days, keeping up with the onslaught of incoming requests is nothing short of exhausting. Don’t leave your IT personnel out to dry by making them handle this monumental task manually. Arm them with the technology they need to do their jobs better, faster, more efficiently and more effectively, such as IT automation.

Provide a safe place to vent. Without question, the job of keeping an entire organization safe from the potentially devastating financial and reputational damage a successful breach can have is incredibly stressful. Additionally, IT personal often feel tense due to the amount of classified and confidential information they are entrusted with. Provide an opportunity and a secure avenue for these employees to vent their feelings.

Encourage time off. Everybody needs a little down time, but given the fast-paced and highly stressful field of IT, these employees could probably use some time off more than anyone else in your organization. This is where technology can help. By automating a good portion of tasks and leveraging the cloud to embrace more flexibility, your team can take the much needed time off they deserve without the company feeling any negative impact.

Use realistic metrics to measure success. One of the biggest reasons IT professionals find themselves dissatisfied at work is because they feel they aren’t being adequately recognized. This is often due to a lack of clear and specific metrics for success. Management should set realistic expectations, communicate openly and routinely measure progress. Good work should be rewarded and areas of improvement identified and addressed in a positive, productive way.

Empower them. If your employees feel that their only option is to come in every day and put in 10-12 hours of labor, they’re not going to develop any kind of connection or loyalty to your organization. On the other hand, if they know that the work they do plays a direct role in the “big picture” and that their achievements are tied into the company’s overall success, they’ll be much more plugged in, which means they’re more likely to stay on for the long haul. Empower them by inviting ideas and encouraging autonomy.

Are you doing enough to keep your IT personnel satisfied, engaged and plugged in? If not, you could be facing higher turnover, which can negatively impact your company’s bottom line and also leave you more vulnerable. By implementing the above tips, you’ll create a more positive work environment that fosters longevity. Happy employees will work harder to ensure that your organization remains strong, secure and successful.

Human Learning vs. Machine Learning – What’s the Difference

September 7, 2018/in Artificial Intelligence/Machine Learning, IT Process Automation /by peter lee

These days, artificial intelligence is all around us. If you’ve ever used Siri on your iPhone or the live chat feature of a website, you’ve interacted with AI. From a business perspective, the rise of AI can be both exciting and challenging. Furthermore, it’s a concept that isn’t necessarily easy for everyone to grasp. The most common question being asked by individuals who aren’t deeply involved with tech is, “What, exactly, is artificial intelligence?” Perhaps the easiest way to understand AI is to compare it to something that is already widely understood – human intelligence.

How Does Human Intelligence Work?

Generally speaking, human intelligence follows a simple, straightforward and typically predictable pattern. We gather information. We process that information. And we use that processed information to decide what to do next. These three basic steps can be summed up as follows:

Input —> Processing —> Output

Input occurs through sensing and perceiving the things all around us. The senses – eyes, ears, nose, etc. – gather raw input, such as the sight of light or the scent of a flower. The brain then processes that information and uses it to determine what action to take. In the processing stage, knowledge is formed, memories are retrieved, and inferences and decisions are made. Output then occurs in action based on the information processed. For instance, you might hear a siren, see an ambulance in your rear view mirror and subsequently decide to pull over to let it pass.

In order to safely navigate the world in which we live, we must effectively process all of the input we receive. This basic concept is the core of human intelligence, which can further be broken down into three definitive segments:

Knowledge/Memory

People gain knowledge through the ingestion of facts (i.e. the Pilgrims landed in 1620) as well as social norms (i.e. saying “Please” or “Excuse me”). Further, memory allows us to recall information from the past and apply it to present situations.

Inference/Decision

Inferences and decisions are made based on the raw input we receive, combined with our memories and/or built up knowledge. For instance, let’s say you tried a new food a few months ago that turned out to be way too spicy for your taste. The next time you’re offered that food, you politely decline.

Learning

There are a number of ways humans can learn, including observation, example and algorithm. With observation, we determine the outcome on our own. With example, we are told the outcome. Learning by algorithm, on the other hand, allows us to complete a task by following a series of steps. A good example of this would be solving a long division problem.

Human Learning vs. Machine Learning

The main aspects of human intelligence are actually quite similar to artificial intelligence. In the same way that humans gather information, process it and determine an output, machines can do this as well.

Of course, because machines do not have physical senses like people do, the way they gather input differs. For instance, rather than sight or smell, artificial intelligence gathers information through things like speech recognition, visual recognition and other data sources. Think about how a self-driving vehicle can sense obstacles in the roadway or how your Amazon Echo listens and recognizes your voice.

The processing piece of the formula also mimics how human intelligence works. Similar to the way people accrue memories and build knowledge, machines are capable of creating representations of knowledge and databases where information is stored. And, just as people draw inferences and make decisions, machines can predict, optimize and determine what the best ‘next steps’ should be in order to accomplish a particular goal.

Similarly, just as humans learn by either observation, example or algorithm, machines can also be “taught.” For instance, supervised machine learning is akin to learning by example: the computer is provided with a data set containing labels that act as answers. Over time the machine can essentially “learn” to differentiate between those labels to produce the correct outcome.

Unsupervised machine learning is like learning by observation. The computer recognizes and identifies certain patterns and subsequently learns how to distinguish groups and patterns on its own. Lastly, learning by algorithm is the process by which a programmer “instructs” the computer precisely what to do, line by line, using a software program. Ideally, the most effective form of artificial intelligence will utilize a combination of the above learning methods.

The output that results sums up how machines interact with the world around them, whether it’s speech generation, navigation, robotics, etc.

Take, for example, the business use case of cybersecurity threat detection. Artificial intelligence can scan enormous amounts of data and monitor an entire infrastructure in real-time. It can then, through a combination of unsupervised and algorithmic learning, pinpoint anomalies that could potentially represent data breaches. It can then use that information to investigate and test, automatically determining what the next steps should be, whether it’s escalation to a human agent or automatic remediation.

The Future is Now

We have, undoubtedly, only seen the tip of the iceberg as it relates to artificial intelligence and its potential impact on our lives – both personal and professional. As technology continues to evolve and improve at a breakneck speed, AI and machine learning capabilities will also evolve. Why wait? Get ahead of the curve and experience the next generation of automation and AI by taking Ayehu for a test drive today.

The Future of IT Support

September 4, 2018/1 Comment/in Artificial Intelligence/Machine Learning, ITSM Automation /by Gabby Nizri

Last year, the folks over at Service Desk Institute (SDI) published an in-depth report projecting the future of IT support entitled “Analyst 2.0.” The report spans a number of areas, including the way IT service desk analyst skills and staffing are evolving as well as the growth and impact of automation and self-service technologies. If you’re curious about what the IT support of tomorrow will look like but haven’t got time to dig into the full report, this blog should help by providing a broad overview of its finding, focused primarily in three key areas:

Skill requirements of future service desk analysts
Artificial intelligence (AI) adoption thus far
Service desk challenges as they relate to customer experience

Let’s take a more detailed look at each of these points below.

Skill requirements of future service desk analysts

The SDI report drew contrast between the top three skills that are currently require of service desk analysts and those that will be expected over the next three to five years.

Skills needed in 2017…

Skills needed in 2020 and beyond…

While the increase in analysts’ customer service/empathy skills may not come as much of a surprise, some of the other projected changes may. In particular, the marked jump of tech industry knowledge from 34% to 67% and the ability to be flexible rising from 8% to 27%.

Both of these things are indicative of the need for IT support analysts to know and be capable of performing more complex tasks, particularly in light of the fact that self-service and automation will be eliminating the simpler, more repetitive tasks from their to-do lists.

Further, the increasing need for adaptability and flexibility signify the fact that the service desk will experience frequent and ongoing change.

Artificial intelligence (AI) adoption thus far

Of those surveyed for the SDI report, 27% indicated that they’ve begun the process of researching AI or virtual assistants for their organizations. A smaller percentage is either interacting with potential partners or have already implemented AI to some degree. While trends indicate that these numbers have already risen since the date of the SDI report’s publication, it still appears that the current use of AI in a help desk capacity hasn’t yet caught up with the industry buzz surrounding the technology.

There may be several reasons why this is the case, including the fact that the opportunities for and benefits of AI for IT support haven’t been adequately “sold” to the market (something we are working hard to change here at Ayehu). Other hindrances may include budgetary restraints and fear of change.

A second, perhaps more telling question asked of survey participants was whether or not they felt technology is keeping up with the “hype” surrounding AI and automation. The responses were an even split between yes and no (both at 29%). The rest indicated that they were unsure.

Service desk challenges as they relate to customer experience

The third noteworthy component of the SDI report involved a particular question relating to the customer experience. Specifically, participants were asked: “Do you feel pressured to provide the same level of customer service as big businesses?”

The response is demonstrated here:

As you can see, there is significant pressure (whether real or perceived) on corporate IT support teams that extends far beyond the “consumerization of IT.” Service desk leaders need to respond to these pressures accordingly. In particular, adopting self-service and automation can improve the end-user experience and alleviate some of the pressures agents are facing. It will also free up skilled IT staff to focus on more meaningful and fulfilling work, boosting morale in the process.

These are really just a few of the many intriguing points uncovered by the SDI report. You can view the full report here for free.

And if you’re ready to start preparing your support desk for the wave of the future, AI and automation are the place to be. Experience the power of artificial intelligence and machine learning capabilities by taking Ayehu for a test drive today.

Ayehu Launches Intelligent Automation Radio Podcast

August 30, 2018/in Artificial Intelligence/Machine Learning, News & Events /by Evelyn Kotler

Business Technology Podcast Features Executive Thought Leaders’ Mind Sharing and Insights, Exploring the Intersection of Intelligent Automation and the Next Generation Enterprise

Ayehu, provider of the leading AI-powered IT and security automation and orchestration platform, today announced that it has launched a new podcast series called Intelligent Automation Radio. The podcast will air monthly and feature executive level interviews covering topics addressing the most advanced technology innovation and trends impacting today’s enterprises, including the future of work, artificial intelligence, automation, cyber security, IoT and more.

Intelligent Automation Radio explores the issues and opportunities companies are facing as they cross the digital divide and move toward becoming self-driving enterprises. Tune in to hear from technology visionaries, Fortune 500 corporate executives, CIOs and CSIOs, industry experts and other luminaries.

“We are at the dawn of the 4^th Industrial Revolution, with automation, artificial intelligence, and other technologies changing how the world works,” said Guy Nadivi, Intelligent Automation Radio Host and Senior Director of Customer Success, Ayehu. “As these technologies become more intelligent, they offer game-changing opportunities for savvy C-Suite executives to leapfrog their competition.”

Join the first opening episode for the fall 2018 season, an exciting conversation with Robert H. Brown, Cognizant Center for the Future of Work. Hear about the intersection of technology, people, and the jobs that are being profoundly impacted by advances in automation and AI. Brown comments on how businesses and jobs will be evolving in the digital economy.

Future episodes will delve into additional areas pertaining to digital transformation innovations such as deep learning, data science, analytics, security automation, robotic process automation, Internet of Things, blockchain, and more. Intelligent Automation Radio invites CIOs, technologists, and others to share their first-hand experience and thoughts about their outlook on the future.

To listen to the podcast, visit https://ayehu.com/podcast.

Listener feedback and suggestions are welcome. If you would like to be considered as a potential future guest on the show, or would like to suggest any topics to be covered, please contact podcast host Guy Nadivi at guy@ayehu.com.

About Ayehu

Ayehu’s AI-powered automation and orchestration platform is a force multiplier for IT and security operations, helping enterprises save time on manual and repetitive tasks, accelerate mean time to resolution, and maintain greater control over IT infrastructure. Trusted by hundreds of major enterprises and leading technology solution and service partners, Ayehu supports thousands of automated processes across the globe. For more information, please visit www.ayehu.com and the company blog. Follow Ayehu on Twitter and LinkedIn.

PR Contact:

Christy Kemp
Dahlia Public Relations
303-898-3390
ckemp@dahliapr.com

Leveraging Intelligent Automation to Bridge the Skills Gap

August 27, 2018/in Artificial Intelligence/Machine Learning, IT Process Automation /by peter lee

When it comes to digital transformation, certain distinct skillsets are needed – of which many are in short supply. The area of cybersecurity, for instance, is suffering a remarkable shortage of talent. IT operations that focus on human capital and disparate tools and systems simply won’t be enough to keep up with the staggering pace of innovation.

Modern enterprises must be capable of adapting quickly to the ever-changing and increasingly complex environment while also remaining flexible. Furthermore, a growing number of IT technologies, applications, systems and processes must be adopted and routinely updated in order for organizations to remain competitive.

These demands pose a serious challenge to those enterprises that do not have adequate talent or expertise. For those IT teams that find themselves behind the eight ball, intelligent automation can be their ace in the hole.

The Shift from Human to Machine

Gartner predicts that by 2020, 75% of enterprises will experience visible business disruption due to skills gaps. This is up dramatically from just 20% in 2016. This is a serious concern for business leaders across all industries.

In response, many organizations are already working to add technologies that can augment their existing human resources. In particular, intelligent automation and orchestration is becoming a significant focus. In fact, Gartner lists AI and machine learning strategy development/investment among “the top five CIO priorities.”

Making the shift from human to machine results in two distinct advantages. One, because intelligent automation is capable of performing massive amounts of error-free work, productivity skyrockets. Second, with the addition of intelligent automation, existing human workers can apply their advanced skills to more important business initiatives, such as growth and innovation. And thanks to machine learning and AI technologies, decision-makers can avail themselves of data-driven support.

A Match Made in IT Heaven

With intelligent automation, organizations facing the challenge of budgetary restraints can build highly functioning, agile IT operations without the need to hire additional staff. Existing personnel can be trained and reskilled to become versatilists — those who can hold multiple roles, most of which will be business, rather than technology, related.

“The key to delivering digital value at scale is having the right people talent,” says Terrence Cosgrove, research vice president at Gartner. “Currently there just isn’t enough talent with the digital dexterity for hire, so I&O leaders will need to develop this core competency in the talent they already have.”

With the help of intelligent automation, IT departments can operate at maximum efficiency, saving time and money in the process. In fact, this technology has the potential to position forward-thinking enterprises at the forefront of digital transformation, despite the growing talent shortage.

Could your organization benefit from this “ace in the hole?” Find out today by taking Ayehu for a test drive.

The CAO’s Role in Digital Transformation

August 22, 2018/in Artificial Intelligence/Machine Learning, IT Process Automation /by Gabby Nizri

Today’s savvy business leaders recognize the need to adapt to changing technology or risk being left behind. And while we may not be able to control what that technology will be or which direction it will take us, we can take advantage by responding quickly and making the necessary adaptations before our competitors. That’s why the role of Chief Automation Officer is becoming much more necessary.

A recent study by PMG revealed that the biggest obstacle to successful adoption of automation was the lack of a holistic approach. In fact, 59 percent of respondents agreed that automation implemented on an ad hoc basis leads to incompatibility and subsequent inefficiency. Most organizations today require not only a unification of silos but also the ability to align automation strategies between different physical locations, some of which may be situated half a world away.

How can one expect to effectively align IT functions with the business as a whole when automation – the very technology that’s designed to enable better business decisions – is not adequately represented in the C-Suite? A CAO brings valuable perspective with the ability to see automation from a global viewpoint. As a result, automation becomes a critical component of the overall strategy rather than just a means to deliver it.

Organizations with multiple technology centers located worldwide recognize the importance of global collaboration to the forward progress of the company. There is little overall business value if you’ve achieved continuous process improvement in just one office but not the others. You may have certain specialists deployed across the globe who are highly skilled at managing automation technology, but if there’s no central strategy and one leader, their abilities cannot be maximized.

The CAO’s purpose is to drive this type of simultaneous widespread orchestration. While automation specialists focus on their specific IT departments, managing the day to day operations, the Chief Automation Officer spends his or her time looking outward and forward. This role is absolutely essential to achieving rapid, sustainable transformation, as the ability to orchestrate automation on a global scale and unify strategies is what ultimately enables businesses to adapt at the speed of thought.

Automation is critical to an organization’s ability to reallocate capital from today’s business operations to the needs of tomorrow. It allows IT to shift from reactively putting out fires to taking a more proactive approach, which is necessary for successful digital transformation.

The good news is automation is evolving at relatively the same pace as the complexity it is designed to simplify. Disruption that would otherwise be overwhelming is now becoming entirely feasible thanks to automation and orchestration solutions that provide a competitive edge. Deriving business value from the IoT, for instance, is made possible by automation strategies that process and analyze the data being procured. Automation essentially frees up funds that can then be used to fuel innovation.

The CAO is responsible for taking optimized workflows and applying them wherever feasible throughout the entire organization. This creates more compliant and easily auditable processes. And because this approach eliminates repetition and duplication of work, the entire operation can run more consistently and efficiently. Furthermore, by evaluating how automation is utilized throughout the company, the CAO is also able to optimize the allocation of human resources, removing bottlenecks and improving productivity across the enterprise.

Another area where the CAO can be highly beneficial to the company is in his or her ability to seek inspiration from the rest of the workforce. One company was able to uncover nearly 300 innovative ideas simply by asking employees to suggest processes to automate. In the absence of this communication between upper management and frontline workers, these suggestions, and the positive changes they brought about, would have remained private.

In conclusion, automation remains a catalyst for change, driver of innovation and procurer of productivity. With the overarching visibility and control a CAO provides, automation strategies will have the flexibility and scope needed to stimulate disruption and achieve digital transformation.

Want to see the power of automation and orchestration powered by machine learning in action? Click here to request a product demo.

How Intelligent Process Automation is Revolutionizing the Financial Field

August 20, 2018/in Artificial Intelligence/Machine Learning, IT Process Automation /by Gabby Nizri

Just as robots have been performing routine, repetitive tasks on the floors of factories for decades, that same concept is now being applied to the financial industry with tremendous results. Intelligent process automation (IPA), powered by AI and machine learning technologies, promises to revolutionize how financial organizations operate, maximizing efficiency, cutting costs and ensuring optimal service levels.

This advanced technology has the potential to transform just about every area of the finance function. In particular, IPA will enable finance professionals to make the critical shift from transactional to strategic.

When rote, manual tasks are automated, skilled finance experts will spend less of their time running reports and performing provisional data analyses. Instead, they will be able to quickly and easily gather and analyze the essential data they need to enhance performance and deliver better support. No longer bogged down with busy-work, these experts will have the time to engage with other lines of business and deliver the forward-thinking guidance that leadership needs to pursue new opportunities.

Let’s take a look at a few real-life examples of how intelligent process automation can be applied in financial institutions.

Account Reconciliations

A good portion of today’s accounting teams spend far too much time performing manual reconciliations across a variety of systems. At the enterprise level, this can involve thousands or tens of thousands of individual transactions across multiple systems, both internal and external.

In reality, many of the mismatches found in reconciliations actually follow similar patterns. For instance, an entry was made twice in error or someone accidentally keyed in a negative sign instead of a positive. Intelligent process automation is capable of not only recognizing these patterns, but also identifying the issue at hand and correcting it. In many cases, this can be done without the need for any human input.

Some estimates indicate that up to 80% of these simple reconciliation tasks can be automated, freeing up skilled staff to focus their efforts on the smaller percentage that are more complex or require human judgment.

Closings

For most corporations, the end of the month or quarter is a stressful time that requires all hands on deck to handle the financial close. This process typically involves a number of different systems, multiple finance teams and several lines of business, there are a multitude of dependencies that require tracking and management. For instance, certain accounts cannot be closed until all related sub-accounts are closed by the subsidiaries.

Intelligent process automation can alleviate much of this complexity by:

Automatically tracking the status of completed tasks across multiple systems
Automatically initiating close processes upon completion of dependent tasks
Updating the close calendar to provide visibility into the status of the financial close process

Financial Planning & Analysis

FP&A is another complex but necessary process eating up a significant portion of time, particularly for senior executives. It involves creating and approving budgets, analyzing spend, managing those budgets, etc. With IPA, routine budget requests that follow a common pattern don’t need to be signed off on. Instead, only unfamiliar or unusual requests will require review and approval.

Prior to submitting a request for executive approval, the intelligent process automation platform can analyze the pattern (i.e. dollar amount, similar requests that were made in the past, etc.). If it determines that similar requests received 100% approval in the past, the software can automatically process the approval without the need for human intervention. If an anomoly is detected, it can reroute to an executive for manual review.

PwC predicts that by 2020, financial services organization will begin to ramp up their AI adoption, streamlining operations and re-shoring jobs. And given the tremendous benefits intelligent process automation has to offer, it’s obvious why.

Want to position your financial organization for future success? Jump on the IPA train! Click here for a free, interactive product demo and get started on the path to digital revolution today.

5 Practical Business Applications for Machine Learning

August 17, 2018/in Artificial Intelligence/Machine Learning, IT Process Automation /by Gabby Nizri

Today’s forward-thinking organizations are leveraging the power of artificial intelligence to automate the decision making process. In fact, corporate investment in AI is predicted to reach $100 billion by the year 2025. As a result of this rapid digital transformation, many changes are underway in the workplace. In particular, there are a number of ways that machine learning is already making an impact for companies in every industry. Here are a few to consider.

Personalizing the customer experience.

One of the most exciting benefits machine learning can have for businesses is the fact that it can help improve the customer experience while also lowering costs. Through things like deep data mining, natural language processing and continuous learning algorithms, customers can receive highly personalized support with little to no human intervention. And people are warming to the idea. In fact, 44% of US consumers say they actually prefer chatbots to human agents.

Improving loyalty and retention.

With machine learning, companies can do a deep dive into customer behavior to identify those who are at a higher risk of churning. This enables organizations to develop and implement next steps designed to target and retain those high-risk customers. The more proactive a company is in this area, the more profitable it will be over time.

Enhancing the hiring process.

When asked about the most difficult part of their job, corporate recruiters and hiring managers almost unanimously list the task of shortlisting qualified candidates for job openings. With dozens and sometimes even hundreds of applicants, sifting through and narrowing down the options can be a monumental task. Machine learning is fundamentally changing the way this process is handled by letting software do the dirty work, quickly identifying and shortlisting those candidates that are the best fit.

Detecting fraud.

Did you know that the average organization loses up to 5% of their total revenues each year due to fraud? Machine learning algorithms can be used to track data and apply pattern recognition to identify anomalies. This can help risk management detect fraudulent transactions in real-time so they can be prevented. This type of “algorithmic security” can also be applied to cybersecurity, leveraging AI to quickly and accurately pinpoint threats so they can be addressed before they are able to do damage.

Streamlining IT operations.

Another way AI and machine learning are revolutionizing how organizations operate is through intelligent IT automation. Powered by machine learning algorithms, agentless automation and orchestration platforms become force multipliers, driving efficiency and helping enterprises save time on manual and repetitive tasks, accelerate mean time to resolution, and maintain greater control over IT infrastructure.